feature-dev

A step-by-step process for adding a significant feature to a software project. It covers understanding the request, exploring the existing code, designing the change, building it, checking the result, and summarising the work.

In plain words
What is it for?
It helps plan and implement new features, resolve unclear requirements, review the result, and document what changed.
Why use it?
It reduces the chance of coding before the requirements and surrounding code are understood. It also makes review and completion part of the same workflow.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/sequenzia/agent-alchemy/feature-dev
Any agent
npx skills add sequenzia/agent-alchemy --skill feature-dev
Clone the repo
git clone --depth 1 https://github.com/sequenzia/agent-alchemy

Made for: Claude Code, Codex.

Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,277 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00026 $0.02277
Opus 5 $0.00013 $0.01138
Sonnet 5 $0.00005 $0.00455
Haiku 4.5 $0.00003 $0.00228

Measured 3d ago against content hash 4b60f2300570, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

feature-dev scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

claude/dev-tools/skills/feature-dev/SKILL.md · 276 lines

How it starts

The opening of the file, as written. The whole thing — 276 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Feature Development Workflow

Execute a structured 7-phase feature development workflow. This workflow guides you through understanding, exploring, designing, implementing, and reviewing a feature.

CRITICAL: Complete ALL 7 phases. The workflow is not complete until Phase 7: Summary is finished. After completing each phase, immediately proceed to the next phase without waiting for user prompts.

Phase Overview

Execute these phases in order, completing ALL of them:

  1. Discovery - Understand the feature requirements
  2. Codebase Exploration - Map relevant code areas
  3. Clarifying Questions - Resolve ambiguities
  4. Architecture Design - Design the implementation approach
  5. Implementation - Build the feature
  6. Quality Review - Review for issues
  7. Summary - Document accomplishments

Phase 1: Discovery

Goal: Understand what the user wants to build.

  1. Analyze the feature description from $ARGUMENTS:

    • What is the core functionality?
    • What are the expected inputs and outputs?
    • Are there any constraints mentioned?
    • What success criteria can you infer?
  2. Summarize your understanding to the user. Use AskUserQuestion to confirm if your understanding is correct before proceeding.


Phase 2: Codebase Exploration

Goal: Understand the relevant parts of the codebase.

  1. Run deep-analysis workflow:

    • Read ${CLAUDE_PLUGIN_ROOT}/../core-tools/skills/deep-analysis/SKILL.md and follow its workflow
    • Pass the feature description from Phase 1 as the analysis context
    • This handles reconnaissance, team planning, approval (auto-approved when skill-invoked), team creation, parallel exploration (code-explorer agents), and synthesis (code-synthesizer agent)
    • Note: Deep-analysis may return cached results if a valid exploration cache exists. In skill-invoked mode, cache hits are auto-accepted — this is expected behavior that avoids redundant exploration.
  2. Present the synthesized analysis to the user.

Read the full file on GitHub · 276 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 276 lines · 26 tokens per session scan A 4b60f2300570

Subscribe to this mod's changes

feature-dev is a skill published in the GitHub repository sequenzia/agent-alchemy (43 stars, last pushed 3mo ago), licensed MIT. It adds 26 tokens to every session and 2,277 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

rulesync

Generates and syncs AI rule configuration files (.cursorrules, CLAUDE.md, copilot-instructions.md) across 20+ coding tools from a single source. Use when syncing AI rules, running rulesync commands, importing or generating rule files, or managing shared AI coding configurations.

dyoshikawa/rulesync · 64 tokens

agent-workspace-linux

Use when a task needs an isolated hidden Linux desktop or workspace-owned browser: GUI app QA, web/browser/shopping automation, sandboxed app observation, or stale workspace cleanup. Routes agent-workspace-linux MCP tools on demand. Does NOT apply to host desktop/Chrome control, generic MCP setup, or pure code/file…

ilysenko/codex-desktop-linux · 70 tokens

ss-component

Generate a new UI component following the StyleSeed design conventions.

bitjaru/styleseed · 14 tokens

loongsuite-pilot-insight

基于 LoongSuite Pilot / AI Coding Agent 日志生成事件洞察、组织洞察、数据质量、研发效能和 AI Native 使用类 SLS 报表时使用;包含 AI Coding 事件表语义,以及团队报表可选的部门维表、deptuser 组织关系、指标口径和公共 CTE,通常与 sls-dashboard-builder 一起使用。.

alibaba/loongsuite-pilot · 91 tokens

map-review

Interactive 4-section code review using monitor, predictor, and evaluator agents plus the user and maintainer role reviewers on current changes. Use when reviewing a diff, PR, or staged work before merge. Do NOT use to plan or implement; use map-plan or map-efficient.

azalio/map-framework · 58 tokens

map-fast

Minimal workflow for small, low-risk changes — no planning, no learning.

azalio/map-framework · 17 tokens